Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK.
Appl Health Econ Health Policy. 2012 May 1;10(3):145-62. doi: 10.2165/11599250-000000000-00000.
One of the challenges when undertaking economic evaluations of weight management interventions is to adequately assess future health impacts. Clinical trials commonly measure impacts using surrogate outcomes, such as reductions in body mass index, and investigators need to decide how these can best be used to predict future health effects. Since obesity is associated with an increased risk of numerous chronic diseases occurring at different future time points, modelling is needed for predictions.
To assess the methods used in economic evaluations to determine health impacts of weight management interventions and to investigate whether differences in methods affect the cost-effectiveness estimates.
Eight databases were systematically searched. Included studies were categorized according to a decision analytic approach and effect measures incorporated.
A total of 44 articles were included; 21 evaluated behavioural interventions, 12 evaluated surgical procedures and 11 evaluated pharmacological compounds. Of the 27 papers that estimated future impacts, eleven used Markov modelling, seven used a decision tree, five used a mathematical application, two used patient-level simulation and the modelling method was unclear in two papers. The most common types of effects included were co-morbidity treatment costs, heath-related quality of life due to weight loss and gain in survival. Only 12 of the studies included heath-related quality of life gains due to reduced co-morbidities and only one study included productivity gains. Despite consensus that trial-based analysis on its own is inadequate in guiding resource allocation decisions, it was used in 39% of the studies. Several of the modelling papers used model structures not suitable for chronic diseases with changing health risks. Three studies concluded that the intervention dominated standard care; meaning that it generated more quality-adjusted life-years (QALYs) for less cost. The incremental costs per QALY gained varied from $US235 to $US56,836 in the remaining studies using this outcome measure. An implicit hypothesis of the review was that studies including long-term health effects would illustrate greater cost effectiveness compared with trial-based studies. This hypothesis is partly confirmed with three studies arriving at dominating results, as these reach their conclusion from modelling future co-morbidity treatment cost savings. However, for the remaining studies there is little indication that decision-analytic modelling disparities explain the differences.
This is the first literature review comparing methods used in economic evaluations of weight management interventions, and it is the first time that observed differences in study results are addressed with a view to methodological explanations. We conclude that many studies have methodological deficiencies and we urge analysts to follow recommended practices and use models capable of depicting long-term health consequences.
在评估体重管理干预措施的经济效果时，面临的挑战之一是充分评估未来的健康影响。临床试验通常使用替代指标来衡量影响，例如体重指数的降低，研究人员需要确定如何最好地利用这些指标来预测未来的健康效果。由于肥胖与多种慢性疾病的发生风险增加有关，因此需要进行建模以进行预测。
评估经济评估中用于确定体重管理干预措施健康影响的方法，并探讨方法的差异是否会影响成本效益估计。
系统地检索了 8 个数据库。根据决策分析方法和纳入的效应指标对纳入的研究进行了分类。
共纳入 44 篇文章；21 篇评估行为干预，12 篇评估手术程序，11 篇评估药物化合物。在 27 篇估计未来影响的论文中，11 篇使用了马尔可夫模型，7 篇使用了决策树，5 篇使用了数学应用，2 篇使用了患者水平模拟，2 篇论文的建模方法不明确。最常见的影响类型包括共患病治疗成本、减肥和生存带来的健康相关生活质量。只有 12 项研究纳入了因减少共患病而带来的健康相关生活质量获益，只有一项研究纳入了生产力获益。尽管共识认为仅凭试验分析不足以指导资源分配决策，但仍有 39%的研究使用了这种方法。一些建模论文使用了不适合具有不断变化健康风险的慢性疾病的模型结构。三项研究得出结论认为，干预措施优于标准护理，即它以更低的成本带来更多的质量调整生命年（QALY）。在使用该结果指标的其余研究中，增量成本每获得一个 QALY 从 235 美元到 56836 美元不等。本综述的一个隐含假设是，包括长期健康影响的研究将比基于试验的研究显示出更高的成本效益。有三项研究得出了主导性结果，这部分证实了这一假设，因为这些研究从建模未来共患病治疗成本节约中得出了结论。然而，对于其余的研究，几乎没有迹象表明决策分析建模差异可以解释这些差异。
这是首次对体重管理干预措施经济评估中使用的方法进行比较的文献综述，也是首次针对研究结果的差异提出方法学解释。我们得出的结论是，许多研究存在方法学缺陷，我们敦促分析人员遵循推荐的实践，并使用能够描述长期健康后果的模型。
